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S. Frazier

Did some simple research: please test my thinking:

Ca - Cardiac Arrest
S- Symptons being shortness of breath and chest pains
P(S|Ca) = 53% probability of symptons given cardiac arrest
P(S) = 8% (overall population data, really rough)
P(Ca) = .8% (800/100,000 people suffer Ca)
Using a Bayesian analysis:
P(Ca|S) = P(S|Ca)*P(Ca)/P(S) = 53% x .8%/8% = 5.3%
Your chances of cardiac arrest given the symptons is 5.3%, meaning you may not need to run to the hospital. You certainly need a control group to factor out issues such as panic attacks, etc., that can cause the same symptons.


SF: Thanks for your contribution. Always good to do back of the envelope. If we do a similar analysis on the other symptoms, the number would be even smaller given the much weaker correlation.


Big data doesn't imply a lack of control groups. Lazy analysts don't use the available data to build an appropriate control group.

Lazier journalists re-print this as useful information.


Chris: Big data is mostly observational data and it takes both a lot of time and a lot of statistical expertise to build "appropriate control groups" so I'm not surprised this is not being done. Sometimes you just can't build control groups from existing data. For example, if you launch a new version of an iphone app, Apple is not going to let you keep both new and old versions in the same store; if you want to measure the impact of the new app, you are forced to perform pre-post analysis. Any creation of a control group would require uncomfortably strong assumptions.

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